1332-P: Comparison of Clinical Characteristics of People Living with Latent Autoimmune Diabetes of the Adult (LADA) and Type 1 Diabetes (T1D) in the Canadian BETTER Registry
Bibliographic record
Abstract
Introduction: LADA is known for a slow progression of autoimmune destruction of beta-cells with cases that span a spectrum between classic T1D and T2D phenotypes. To date, lacking a good understanding of LADA has prohibited the release of specific clinical practice guidelines and subsequently an optimal management. Methodology: Cross-sectional analysis of online clinical questionnaires in the Canadian BETTER registry; participants can specify having received a diagnosis of LADA or T1D. Data was analyzed for the first 1464 participants; 131 (9%) reported LADA and 1333 (91%) T1D. Results: At registration; LADA vs T1D were 49 ± 13 y.o. vs 42 ± 15 y.o; 61% vs. 62% females; diabetes duration 10 ± 9 vs. 24 ± 15 years. LADA vs. T1D were older at diagnosis (39 ± 12 vs. 18 ± 12 years old; p<0.001), only half started insulin in first year of diagnosis (52 vs. 97%; p<0.001), more often reported a family history of T1D (46 vs. 32%; p=0.001). For comparable glycemic control (HbA1c < 8.6 mmol/l, <7%, 38 vs. 32%), fewer cases with LADA reported at least an episode in last month of level II hypoglycemia <54 mg/dL (3.0mmol/L) 63% vs. 77%; p= 0.004, severe hypoglycemia rates were 8% vs. 12%, p=0.15. Diabetes related hospitalizations were 10% in LADA vs. 6%, p=0.11, insulin pumps and glucagon were less used in LADA. In a matched analysis for diabetes duration and gender (1:1), glycemic control was still statistically comparable in LADA vs. T1D (HbA1c <7%: 38 vs 35%, p=0.12) as well as rates of nephropathy (6% vs. 11%, p=0.22), neuropathy (12% vs. 7%, p=0.23) and retinopathy (6% vs. 9%, p=0.07) yet with more cardiovascular disease (5% vs. 2%; p=0.02) and less coeliac disease (3 vs. 5%; p=0.001). Data analysis of 3000 participants is ongoing in early 2023. Conclusion: Reported differences may impact LADA management. This study sets the stage for building a prospective Canadian cohort of LADA to undergo deep phenotyping, genotyping and interventional trials. Disclosure M.Issa: None. A.Brazeau: Other Relationship; Dexcom, Inc., Diabète québec, Ordre des diététistes nutritionnistes du Québec, Research Support; Canadian Institutes of Health Research, Fonds de recherche du Québec en Santé. S.Haag: Other Relationship; Omnipod. A.Roy-fleming: None. V.Messier: None. N.Taleb: Consultant; Viatris Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".